Monetary & Financial Economics

Abstract Every organization needs a performance evaluation system to monitor its progress towards achieving its goals. The absence of such a system implies a lack of communication with the internal and external environment, which could lead to the organization's decline and eventual collapse. This study presents a performance evaluation method for banks, aimed at assessing their status and analyzing the relationships among performance evaluation components. First, performance evaluation criteria were identified using content analysis, and data from nine Iranian banks, including 14 performance ratios, were evaluated. After normalizing the data, fuzzy cognitive maps and particle swarm optimization algorithms were used to determine the weights of the performance indicators. The performance indicators were then estimated. Finally, the entropy weighting method was employed to calculate the performance score for each bank. The results showed that Pasargad and Mellat banks had the best performance during this period, while Dey and Eghtesad Novin banks demonstrated the weakest performance. The research also analyzed the interrelations among various indicators and identified the influence and susceptibility of each indicator in evaluating bank performance.